AI ROI: How to Measure the Business Value of AI Implementation

Before you approve a budget for developing AI, you need to understand:

What will this investment improve, and how will we know it worked?

Maybe you are looking to improve customer response times with an AI assistant, reduce document processing times with an automation tool or improve inventory planning accuracy with a forecasting system.

However, just because the AI can deliver faster response times, fewer clicks and impressive demos doesn’t necessarily mean you will see a financial gain.

By measuring the ROI of your AI, you can understand the business benefits and compare them to the full costs.

If you are looking to hire developers to implement AI then this guide will help you understand how to measure potential gains, what KPIs to measure and if the development proposal will help you achieve your business goals.

AI ROI (return on investment) measures the financial gains you will make from your AI project compared to the total costs.

The formula is:

AI ROI (%) = [(Financial benefits − Total AI costs) ÷ Total AI costs] × 100

So if your AI project has gained you $90,000 in verified financial benefits and costs $60,000 over the same period then your ROI would be 50%.

However, you need to ensure your assumptions are realistic.

You should ensure you can measure:

• Realized savings: Expenses the business actually reduces.

• Additional profit contribution: Attributable additional revenue minus associated variable costs.

• Time saved: Capacity employees can redirect to other work.

• Nonfinancial benefits: Improvements in customer experience, consistency and employee satisfaction.

All of these can be important to measure.

Suppose your AI tool saves your team 200 hours per month.

Multiply that by your hourly employee costs to understand the value of time saved. However, this doesn’t necessarily mean you are saving money.

You should demonstrate how your business is saving money.

Are you reducing overtime costs? Are you avoiding planned contractor costs? Are you processing more paid work?

If employees save time but spending and output remain unchanged, report released capacity rather than a realized financial saving.

This will avoid inflating your ROI based on a great productivity figure.

Ensure you can measure the current cost or performance of the process.

Examples could be:

• Repeated customer queries increasing support costs.

• Manual invoice processing creating delays.

• Slow lead qualification delaying follow-up.

• Internal information searches interrupting productive work.

You should have an outcome in mind.

Instead of saying ‘We will introduce an AI chatbot’ you should say ‘We want to reduce the cost per support query that we successfully resolve whilst keeping customer satisfaction.’

This will give your development team something meaningful to design and measure.

Before development, measure how your process is currently performing.

Depending on the use case you may want to know how many transactions, how long it takes to process, how much it costs in labour, conversion rates, error rates and customer satisfaction.

Ensure you measure over a period of time that reflects how your business normally performs. Take into consideration seasonal factors, unusual demand and varying complexity.

Ensure you document:

• What each KPI measures and where the data comes from.

• Who is responsible for reporting it.

• The measurement period.

• The target improvement.

• The quality standard that must be maintained.

Without measuring a baseline you may not be able to prove the business value, even if your launch is successful.

Your total investment model should include vendor charges and internal business costs. Ask which of the following are included in the development quote and budget separately for the rest:

When comparing custom AI development services, ask for a breakdown of one-off and ongoing costs.

A proposal that excludes integration, evaluation or ongoing support can look cheaper while leaving essential work unbudgeted.

Ensure you have a clear calculation for each gain.

Cost saved on processing:

Number of transactions x Confirmed cost saved per transaction.

Cost saved on reworking:

Number of errors avoided x Average cost saved per error.

Additional profit:

Additional attributable sales x Contribution margin per sale.

Cost avoided externally:

Contractor, overtime or other costs that are clearly not being incurred.

Ensure you don’t double count. If the same hours are being saved on processing and additional output then you can’t claim the full value.

If you’ve seen an improvement in sales since launching your AI it doesn’t necessarily mean it’s because of the AI.

Price changes, marketing campaigns, seasonal demand and changes in staff can all impact results.

Where possible, compare similar groups with and without AI. If you can’t then try to implement AI in phases and compare results. Take other changes into account.

Ensure you include any costs of checking and correcting AI outputs.

For example, if your document process is saving 5 minutes on data entry but adding 4 minutes on review then your improvement is less than you initially thought.

You should have an initial business case for your project but this will be a forecast. Once you’ve launched you can see if your assumptions were correct.

Regularly review how well the solution has been adopted, how much it is costing to run, quality and financial gains. If there are changes to how it is being used, models or processes then increase the frequency of your reviews.

If returns fall below expectations, investigate before expanding:

• Are employees using the solution?

• Is human review taking longer than expected?

• Are integration issues creating extra work?

• Are usage costs higher than forecast?

• Is the workflow too small to justify the investment?

Scale when the evidence supports further investment.

Let’s say you are implementing an AI support assistant.

Illustrative example: These hypothetical figures demonstrate the calculation. They are not Henceforth Solutions client results, a pricing estimate or a guaranteed outcome.

Assume the measurement period includes implementation and the first operating year. For this hypothetical calculation, assume all listed benefits are realized within that period.

Additional profit is assumed to be independently verified and not included in the support cost saving.

AI ROI = [($72,000 − $48,000) ÷ $48,000] × 100 = 50%

This gives a net gain of $24,000.

However, if the gains are less then you could have:

This example keeps the costs the same to show how sensitive it is to the gains. In reality you may see higher gains but also higher running costs due to higher usage.

Before approving a project ask yourself if you can afford the conservative scenario.

To keep it simple:

Simple payback period = Upfront investment ÷ Monthly net financial benefit

If your initial investment is $30,000, monthly benefits are $6,000 and you have monthly ongoing costs of $1,500:

30,000÷(6,000 − $1,500) ≈ 6.7 months

This assumes that you will start seeing gains straight away and they will be consistent. In reality it can take longer due to development time, adoption and inconsistent cash flow. For a more accurate figure track the cumulative cash flow from the first spend on your project.

Ensure it relates to the process.

Also ensure you are measuring adoption. If the tool is performing well in testing but not being used much then it may not provide much value.

As a business owner a failed project can be costly.

You may have delayed launches, more management involvement, frustrated employees and customers going through a process that isn’t working.

This can be frustrating if you thought it would be an affordable project. You may find there are missing integrations, unreliable outputs and unclear ownership after having invested a lot of time.

The answer isn’t to assume that a higher quote will give you better results.

Ask yourself if the scope of the project will give you a realistic chance of seeing value.

Sometimes it can be a good idea to keep it simple. If you know the costs, success criteria and next steps then a smaller, well defined pilot can be a good option.

Ensure you know what would make you want to scale, amend or stop the project. This will make it easier to manage your investment.

Ensure you know what would make you want to scale, amend or stop the project. This will make it easier to manage your investment.

Whilst there is research into AI productivity you should still test it in your environment.

For example, the 2023 version of the NBER working paper Generative AI at Work reported an average productivity improvement of around 14% in the customer support setting studied. Outcomes varied across workers. This was a specific implementation, not a universal forecast for AI projects.

Use external research to understand what is possible but use your process data to understand what you could expect.

You should also consider risk management. The NIST AI Risk Management Framework Playbook outlines suggested best practices under the Govern, Map, Measure & Manage categories. This is voluntary but can be used as a reference.

As a buyer you should ask for evidence beyond just a working demo. Ensure there is documentation, monitoring, clear ownership and a process for failure.

A credible development partner should be able to explain what the opportunity is and what needs to happen to realise it.

When you hire AI developers for business automation, assess their understanding of the workflow, its economics and its operational constraints.

Ask the following questions to compare proposals:

The cost of hiring AI developers matters, but hourly rates alone do not reveal the total investment.

A lower hourly rate may result in a higher overall cost if there is a lot of rework needed. A higher hourly rate doesn’t necessarily mean they are competent. Look at the team, scope, delivery method and evidence.

When evaluating an AI development company for business automation, ask for a scoped proposal tied to an agreed business outcome.

Consider custom development when available products cannot adequately support your integrations, permissions, business rules or user experience. For conversational assistants and knowledge tools, generative AI development services may be relevant; other workflows may need a different approach.

However, if your requirements are standard and you are happy with the quality, security and cost of an existing product then this may be sufficient.

Before hiring developers compare:

1. Improving the existing process without AI.

2. Configuring an available AI product.

3. Developing a custom AI solution.

Evaluate all three against the same business objective and measurement period.

This will help you understand if there is enough value in developing a custom solution.

You don’t need a longer list of AI features to be confident with your investment. You need to have a clear process, realistic costs and agreed success.

At Henceforth Solutions, we offer custom AI services for businesses exploring implementation. Our published development approach starts with understanding your business goals, challenges and existing technology environment.

When preparing to hire developers share:

• The workflow you want to improve.

• Your current workload and operating costs.

• The systems the solution must integrate with.

• Your budget and timeline.

• The outcomes that would justify further investment.

Ready to hire developers for AI implementation? Discuss your AI project with Henceforth Solutions and request a proposal covering scope, integrations, evaluation and ongoing costs.

A stronger investment in AI starts with a measurable business case.